Ecosystems and Capabilities as a Service

7 years ago, I got an invitation from Prof. Wil Van der Aalst to participate in a research project lead by the Fraunhofer university, related with using process mining on process event logs, from different companies. The idea was to benchmark and compare how the a similar process was being executed (e.g.: order to cash) in a way that all the companies involved could learn how its process was behaving vis-à-vis the others. There was also another technical research challenge related on data preparation that allow balanced, not biased, process performance comparison. Data sources were being managed and stored by ERP’s and even on the case the ERP used was the same, challenges related with version, database schema or process flow design were also relevant in the research domain.

In those days, companies were not prepared to share such kind of data, it was considered a loss of competitive advantage to expose and compare performance with competitors. Digital transformation was a strategic concept that was not born yet, that resides on how value chain separation fades and starts to intersect with other value chains of different industry sectors, that enables new business models that were not even possible (in todays environment a oil company can provide electricity poles to recharge electrical vehicles, from distributed energy source provided by an utility company) . To another extent, a company is no longer as a member of a single industry sector but as part of a business ecosystem that crosses a variety of industries.

I am working lately with e-commerce companies in Asia. Despite they still pursue a growth strategy in terms of average customer spending, merchants onboarding, sales increase, they are also looking to transform themselves into a software company. For example an e-commerce company wants to stop to transport an order to the customer, to evolve the shipping capability to streamlining sourcing, planning, execution, settlement and end-to-end transport optimization and introduce responsive packaging systems (critical for food safety) or Just in Time supply integration via real time condition monitoring, meaning, the e-commerce company can start exploring direct supply integration with electronics companies or automotive industry. The more they penetrate into new industries by the evolution of the capability, that can include among others: Collaborative Commerce, Product accountability, Supplier Risk Management, Demand Forecasting, Warehouse Management, Smart Contracts, they are able to grow outside their core business and can start providing Retail-as-a-Service for companies that want to enter in the market they operate, connecting local factories where the products are produced to the front end commerce portals and the underlying delivery to the consumers. The capability is not a property and competitive advantage of the e-commerce company is now shared and sold as a service to other companies.

Other area that I see looming is to re-use technologies that were crafted and used in multiple companies. Some start-ups that operate in the artificial intelligence arena are making of their business model to keep the IP and redeploy it in other customers, in particular A.I. models. Like in the example of the Fraunhofer research project, the objective is to improve the models used – for example: for credit risk scoring, fraud, asset integrity management. Customers require that data keeps private, but they do not mind to reuse models that are being evolved in other companies if they fit and they will seek to substitute for the next version as it progressed and continue to deliver better results.

In this kind ecosystems, companies coevolve capabilities around a new innovation: they work cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovations, as ecosystem evolve and originate other ecosystems, consumers or business users can interact, transact,  for a wide range of offerings, without leaving the ecosystem. For the companies that operate in the ecosystem, this means having access to new revenue sources. To the companies that are served via these new kind of ecosystems, it is also a way of lower cost of ownership, speed to market and access more evolved technologies to operate.

 

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Blogs worth reading 2018

Here it is the anual list of blogs which I normally consume on a random basis for research purposes and to be inspired by others point of view.

On Digital Transformation at large:

  • Cape Gemini blog – A good source to look for trends on digital transformation.
  • Column 2 – Independent source on digital transformation by Sandy Kemsley.
  • IEEE Spectrum – Future technologies brought by IEEE.
  • Jim Sinur – Digital transformation trends by Jim Sinur.
  • PeterVan – A 1.0 version type of blog that curates interesting articles from Peter Vander Auwera.
  • On Web Strategy – Dion Hinchcliffe writes about the human side of digital transformation.
  • Internet of Things and Services Blog – By Bosch Software on IoT.

On Complexity:

 

If you are interested in 2017 list click here.

A year in review 2018

In 2018 I saw the rise of the ecosystem and the commoditization of the IT platform. The IT platform still continues to be the first step or the common denominator to enable digital transformation. It brings interoperability in hybrid models that provides the foundation for not just an Internet of things, but an Internet of things, people, and services working together as well as new kind of technologies like mixed reality and cognitive services by bringing together human, machines, and artificial intelligence. It allows to process the complexity of machine-generated signals, large amounts of customer data, with better and faster analysis of product performance and reach. Nevertheless, a business ecosystem, gradually moves from a random collection of entities that interact or transact to a more structured community, adding more partners, more products, more offers and enlarging the customer base. Of one key success of Amazon is how they grow in terms of product offerings. They started as book store. Today, they sell virtually anything. In business ecosystem, a company manages information and its flows. In terms of data, this is not about our view of customer, that can be expanded how each partner that belongs to ecosystem increases that view of the customer and how it detects new opportunities by having access to a bigger customer based and detailed customer profile customer profile, because data is shared with other companies together. Companies will monetize from each transaction occurred as all the other connected partners, the more the customer buys, the ecosystem grows as the business opportunities. On the other hand, this will allow to evolve business capabilities. If a highly efficient logistics company – that manages a fleet X fold in size and it is serving a wider customer base – joins the ecosystem, all the merchants may if they want, benefit from the new innovative added capability, if you attract fintech to support new ways of doing payments, they will drive new interactions with millennials or digital savvy customers. In a business ecosystem, companies coevolve capabilities around a new innovation: they work cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovation.

In online retail, vertical e-integration is a new paradigm. e-commerce players are already expanding their own brand business into more categories. At the same time, specialized start-ups are selling products such as razor blades or functional foods directly to consumers, often on a subscription or membership basis. By bypassing distributors, these start-ups are able to offer lower prices and sell even small volumes profitably. The music industry is a great example. Today, artists are posting their songs directly onto internet-based music platforms, and in the process avoiding consolidators, distributors, and intermediaries of any kind. Internet of things, edge / fog computing like connected product – toothbrush, kettle, toaster or appliances, can create new business models and revenue models as it is happening with household appliances. Audi, just released an electrical SUV (e-tron) which the new revenue source is selling electrical energy  to charge the batteries that will be billed the customer later.

In manufacturing, new digital operational model are being deployed under the cyber-physical systems concept. Which is the fusion between operational technology and information technology, data created by the manufacturing equipment, the warehouses, the trucks that deliver the final products, the signals broadcast by the smart packages, that allows to manage a network of factories across multiple locations, in terms of being adaptable in real time managing manufacturing capacity, based on customer’s orders or comparing performance and expanding continuous quality improvement circles based on the data that is made available from the shop floor. The hype related with self-healing systems is a reality. Instead of humans constantly fine tuning machines when product is being produced out of specification, it is possible to implement runtime procedures that will act on machine parameters when it is predicted that quality specifications will be missed. Using Machine Learning combined with IoT to understand how an equipment operates and how to fine-tune it’s operation to become more efficient or to ensure product quality features, to start to prescribe machine interaction in terms of automatically recommend actions in terms of machinery parameters if it is necessary to put product features back to specification is also being part of the shop floor digital journey transformation. Despite there are challenges related with data interchange, low latency access to cloud services, there is progress with the emergence of each asset, each machine, component is able to initiate an exchange of data to different levels of the organization ensuring integrated, planning, control, execution, optimization of the value chain, this probably one of the most silent areas which progress is not being reported, but I see finally some real value apearing and being materialized.

2016 entry can be found here – I did not write in 2017 about this.

Talent Management Digital Transformation in Oil & Gas Companies

Technology adoption is pushing the workforce to re-skill

Oil & Gas companies like utilities, mining and other natural resources industries, were challenged with barrier of ageing workforce. This actually have an impact. Project overruns, lower asset availability and low production targets. But during this last 5 years, what become some experiments around IoT projects, big analytics about operational performance and most recently, real time collaboration on seismic data studies and reservoir simulation, increased relying on technology – shifting from operational technology to information technology.

Oil & Gas companies need more and more personal with computer science background to keep operations going . Therefore, there is a rise in demand of data scientists, statisticians, individuals that know how to use Artificial Intelligence technologies. This requires a different breed of human talent.

Companies need to deliver to a multi-generational / cultural workforce that demands more flexible work options. And that means it is necessary to be agile in adapting human resource practices to meet the needs of the business and the workforce.  Practically speaking, HR’s role is expanding beyond its traditional focus.

From the organizational side, a new approach to education, and especially up-skilling and re-skilling, is necessary to support digitally enabled workforce and the evolution of new business operations that require with latest technology developments. For example, today it is possible to automate pipeline inspection using drones and combining edge computing solutions [1]. As such, this presents a huge growth area of new jobs opportunities that candidates could apply for or existing workers could take new training go gain role readiness.

From the labor market side, there is the ambition of have much more flexibility in the terms work is performed, mimic the work pattern habits, like in personal life – utilization of social media style tools for internal communications and bring your own device. And a part of that, new generational workforce like the Millennials believe oil & gas companies is lacking innovation, agility and creativity, as well as, opportunities to engage in meaningful work. Strategically “employee value proposition” and pull-factors will attract the most talented. Improving brand awareness captivating candidates to apply for a job opening. Attracting the next wave of the workforce can also include using new approaches like “Recruitainment” – gamification in talent management, that includes among others, recreating a real work environment and understand how well the candidate fits into role execution [2]; [3].

Creating the workforce of the future involves to consider the combination of the following dimensions:

  • Modern workplace – which the workforce can collaborate across multidimension kind of data, using the device and the platform of choice. Contrary to other industry sectors, oil & gas companies are data intensive and data tasks oriented that use neural deep learning network algorithms, fuzzy logic, and others to improve data analysis.
  • HR departments, must rethink how core processes are designed. Artificial Intelligence, when combined with other available technologies can free up considerably of a recruiter’s job and can then use time more efficiently attract the best candidates. Video interview is on the loom, as well as, application and job description matching, eliminating unconscious bias. Plan and predict workforce capacity, upskilling needs and employee disengagement can also be supported by AI technologies.
  • Workforce safety will see a significant leap forward due to digitalization. With digital oilfields and the automation of dangerous tasks, fewer workers will be placed at risk. In addition, Artificial Intelligence will reduce the possibility of human error, contributing further to workforce safety. An example of using AI in field inspections is Equinor (formerly Statoil) – Statoil digital field worker a company that is pushing boundaries in terms of digital transformation.

Contrary to the power shift that occurred towards the customer related with consumer industries, in labor market – unless there is talent shortage – the bargaining power is still much on the recruiter side. However, HR workforce, is aware that it is needed to be investing more in recruiting and retaining the best talent, due to the fact that oil & gas companies are becoming IT companies with a necessity of becoming, fast, a data driven company. For example, in scenarios related with  optimizing drilling, by using historical drilling data, it is feasible to quantitatively identify best and worst practices that impact the target. Advanced analytical methodologies would ultimately develop a model that would provide early warning of deviations from best practices, lessons learned or other events that will adversely impact drilling time or costs. Hence, on one hand there is technological dimension related with what IT solutions are best fit to build the models and make the analysis.

New set of technologies for the evolution of Talent Management

On evolving HR function from hire to retire, increasing hiring quality, contributing to knowledge diffusion and retain talent, HR professionals should consider to invest in these new set of technologies taking in to consideration their priorities.

Talent

Identifying digital enablers for improved talent management

  • Social Sourcing. Storing a static talent pool is not sufficing. Managing a dynamic talent pool that continuously is aware of the activity the candidate is broadcasting in terms of career achievements, new roles or professional activity is becoming ultimately important to spot the changes and trends of the prospective labor market. Social Sourcing is about make it visible a pipeline of sourcing with internal / external candidates integrated with social workforce analytics and from employee service providers (e.g.: LinkedIn). It also includes identify automatically candidate profiles that match your job description requirements.
  • Intelligent Application Screening. Despite of the evolution related with managing job applications in digital mode become a standard, processing an immense volume of applications is not practical and is time consuming. Robotic Process Automation can help with text extraction, candidate profiling and role matching and workflow automation.
  • Automated Candidate Interviewing. This is one of the trends that is moving very fast, in some cases, is becoming fully automated that with the combination of cognitive services (e.g.: image pattern recognition and natural language processing), it is possible to analyze emotions and enhance candidate profiling, makes a pre-screening of the interviews before they are short-listed to the recruiter.
  • Neuroscience Assessment in combination with advanced analytics. Supports decisions about candidate’s degree of fit, matching candidates to positions and at the same time removing recruiter / hiring manager unconscious bias.
  • Mixed Reality. Is able to replicate in the digital world a natural performance environment. It can be used in “Recruitainment”, as well as in training (e.g. safety, operations performance and optimization, geomodeling).
  • Social Business. Provides a platform for finding relevant information, collaborate, ideate, innovate and reach out to colleagues and SME’s. Knowledge diffusion is improved combined with integrated collaboration and productivity tools. – Social Business is a platform that allows to :
    • Know about the Human: Who the worker is, how the worker identify himself and what you pretend to be, the person’s social graph.
    • Human Interactions: What the worker does. Whim whom the worker engages with. How he react to participation in company forums . What kind of work activities are pursued.
    • Search and Analytics: Search for knowledge, gather feedback, get trends, spot patterns, sentiments, learn.
  • e-Learning.  Enable organizations to train and upskill the workforce at scale, with content personalization, and curation, based on particular role execution requisites and performance review feedback.
  • Social performance analysis. Enables workforce analysis in order to improve performance measurement. It also includes, alignment between the nature of work performed and the type of social network configuration the employee is fitted.

The set of technologies uniquely referred above are supported enhanced by a foundational layer composed of:

  • Data Analysis Services. Supports all the moments which decision making is executed, by combining a multitude of data sources. Enables the exploration of data that go beyond those available in spreadsheets. Visualization is possible by direct connection to data sources.
  • Machine Learning. Like in operational improvement scenarios, Machine Learning can be used from predict employee disengagement and the propensity of leaving the organization, to recommend personalized training, and benefits packages;
  • Cognitive Services. Multiple set of Artificial Intelligence technologies supporting workforce planning, automated conversational applications, emotional analysis, recommendation engines and intelligence search:
    • Role description matching and skills / candidate experience.
    • Recommendation engines, used to propose personalized training program, career path, perform workforce impact analysis in terms of future role needs and new skills to be acquired and ultimately;
    • Business Process automation, by the use of chatbots, for candidate or HR staff self-servicing.

Evolving the HR mission and processes by the adoption of Artificial Intelligence, Big data, analytics, and gamification in the hiring process is picking traction, nevertheless, HR professionals also consider the employee experience if talent must be retained and keep low the propensity of leaving the organization.

 

References:

[1] – Gartner defines edge computing solutions as: “facilitate data processing at or near the source of data generation and serve as a decentralized extension of cloud, data center or campus networks. Typical sources of data generation in the context of industrial Internet of Things (IoT) include sensors and control devices such as programmable logic controllers (PLCs) and distributed control systems (DCSs)” – Market Guide for Edge Computing Solutions for Industrial IoT – Gartner 2018. Microsoft’s Azure – IoT Edge is such kind of solution.

[2] – This is not a new trend only in Oil & Gas companies. Marriott hotels, who created a virtual reality Sims-like game in which players have to juggle all the responsibilities of a hotel kitchen manager, was one of the first to try a gamification approach.

[3] – Building the Next Generation of Petrotechnical Professionals through Gamification – Paul Ugoji (Total Automation Concepts Ltd) | Akii Ibhadode (Federal University of Petroleum Resources Effurun, Delta State) | Anslem Amadi (Federal University of Petroleum Resources Effurun, Delta State).

 

 

The man and the machine manifesto – Part II

 

In the man and the machine original ramblings, I alluded to the to the discerning challenge between humans and powered Artificial Intelligence machines will work together, against the “tangled recursion” [14] as an example of machine intelligence. This log entry expands deeper the doubts and wonders about man and machine co-existence.

Cybernetics the need of adopting other forms of intelligence

Cybernetics become popular when the works of the first generation of cyberneticians like Norbert Wiener and Ross Ashby grow reputation from the lectures of Stafford Beer [10] and the works of Gordon Pask on training and teaching machines [11]. Applying the laws and principles of cybernetics, especially the law of requisite variety – control can be obtained only if the variety of the controller is at least as great as the variety of the situation to be controlled [8] – to the design of effective organizations, Stafford Beer formulated the Viable System Model (VSM) [9] as a method for designing organizations that are able to survive and thrive in a changing environment [9]. The VSM was then a vision of the organization in the image of the human species. Functions of management and control were envisioned on the lines of the human brain and nervous system. The brain and nervous system, were simulated by a combination of information technologies and human interaction. The VSM is probably one of the most elaborated representations of the fusion of the man and the machine [12] and the primacy of the role played by information in control systems and decision making. The fusion or combination of man-machine can be considered in form as a cyborg – that has certain physiological and intellectual processes aided or controlled by mechanical, electronic, or computational devices [13].

The limitations or the utopia of singularity

 

 

The achievement of singularity is perceived or motivated by advocators that show fear of death or want to reach a stage of immortality and therefore are irrational, combined with the fact of the looming of the ubiquitous computing, which, computing is made to appear everywhere and anywhere [6] with possible endless workloads combinations, omnipresent, making computing an embedded, invisible part of today’s life. Singularity is perpetually 15 to 25 years in the near future and that future keep being delayed [5]. Singularity supporters, tend to forget about the computing limitations on some of the use cases that are just about to become a reality, like for example, brain simulation. To simulate 10 seconds of real brain time would require about one year of computer simulation [7] in the current most powerful existing supercomputer.

Do we want machines to feel or do we foster a future which humans will augment their natural born capabilities?

 

 

Feelings can only be won by creatures who already have a mind and you can only have a mind if you have a nervous system [1], which in the case of machines, is absent. In the past decade, there an intense debate about if machines can or will feel. Emotions are considered as a non-detachable part of intelligence. Emotions be catalogued as rage, fear, panic, love, happiness and it can drive to take actions. Humans classify emotions and assign them some other emotional value [2]. Such value changes taking into consideration the societal environment humans live or are surrounded by. What can be condemned in one society can be accepted in other. The meaning of the the emotional values is the basis for conscious experience. Therefore, understanding the cartography of the interaction the human and the environment is critical for understanding the nature of consciousness [2]. Our universal individuality is cannot separated by the sacred, profane and spirituality [3]. The flow of other people, nature, environment and ancestor influence that provided us the life principle guidelines we adopt or tend to ignore that is a consequence of what surround us and make us unique individuals. Because machines do not and most likely will not possess consciousness, they are incapable of having free will and intentionality, something that is an essential criteria for moral agency [4].

 

References:

[1] António Damásio – In The Strange Order of Things: Life, Feeling, and the Making of Cultures – ISBN – 9780307908759

[2] Mark Solms – The Brain and the Inner World: An Introduction to the Neuroscience of the Subjective Experience – ISBN – 9781590510179

[3] Elizabeth Tunstall – Decolonizing Design Innovation: Design Anthropology, Critical Anthropology and Indigenous Knowledge in Design Anthropology Theory and Practice – ISBN – 9780857853691

[4] Kenneth Einar Himma – Artificial agency, consciousness, and the criteria for moral agency: What properties must an artificial agent have to be a moral agent?. Ethics and Information Technology, 11(1), 19–29 – ISSN: 13881957

[5] Stuart Armstrong, Kaj Sotala – How We’re Predicting AI or Failing To – In Beyond AI: Artificial Dreams, edited by Jan Romportl, PavelIrcing, Eva Zackova, Michal Polak, and Radek Schuster, 52–75. Pilsen: University of West Bohemia.

[6] Dietmar Möller – Guide to Computing Fundamentals in Cyber-Physical Systems Concepts, Design Methods, and Applications – ISBN – 9783319251769

[7] Arlindo Oliveira – The Digital Mind: How Science is Redefining Humanity – ISBN – 9780262036030

[8] W. R. Ashby, An introduction to cybernetics – ISBN – 9781614277651

[9] Stafford Beer, Brain of the Firm – The Managerial Cybernetics of Organization – ISBN – 9780471948391

[10] Roger Harnden and Allenna Leonard – How Many Grapes Went into the Wine: Stafford Beer on the Art and Science of Holistic Management – ISBN – 978-0471942962

[11] Andrew Pickering – The Cybernetic Brain: Sketches of Another Future – ISBN – 9780226667904

[12] Stafford Beer – Diagnosing the systems for organizations – ISBN – 9780471951360

[13] Woodrow Barfield – Cyber-Humans – Our Future with Machines – ISBN – 9783319250489

[14] Gödel, Escher, Bach: An Eternal Golden Braid – ISBN – 9780465026562

 

From Know Your Customer to Know Your Tax Payer

I have been working with a tax authority on developing some scenarios that were translated into a transformation roadmap with the objective of increasing tax and non-tax revenue.

One of the challenges is to predict active debt management before arrears occur. The idea is using advanced analytics by modelling the risk that company will fail to pay their taxes, by creating clusters of potentially high-risk debtors, based on available information regarding the annual, quarterly and monthly returns filling. One of challenges that exists in this approach, despite being proven that is more effective that discover “after the fact” the existence of a tax debt, is by the constant change in business models, companies makes new investments or shift to new business models that drags quickly the company to a position it cannot comply with its tax payment obligations.

Know Your Customer (KYC), is an approach that is being used, mostly in financial institutions, in terms of opening a bank account, require trading operations or process payments. Some banks are enhancing the concept of KYC to a point that they rely on information that is not related with transactions between the customer and the bank. As I pointed in this previous post, about creating a Banking Platform as a Service, that goes beyond on implementing a new IT capability that can spark new business opportunities and expand the traditional bank value chain, banks today can have a very precise view of a profile of an individual or enterprise. In some KYC scenarios and because of the magnifying effect of such profile. As an example, if you are an individual, instead of asking what was the brand of your first car, they will start asking questions on which part of the globe you were 2 months ago, where you stayed, what brands did you spend money with and what kind of affiliation you have with a 3rd party loyalty program or even if you have contracted loans with other institutions and which under what circumstances such loans were requested.

From a government perspective, using a shared and expanded Know Your Tax Payer (KYTP) is a valuable approach not only to prevent tax arrears or unrecoverable tax debt, as well as uncover misreporting and non-compliance related with the structure of income flows or unreported income or even claiming subsidies or tax deductions regardless of the source. Despite there is a convergence of government data interchange effort, as well as, trying to overcome some legal constraints in terms of sharing tax information across multiple government agencies that own the responsibility of collecting different non-tax revenue sources, it is still clear that there are missed opportunities in terms of securing tax collection.

One of the events that is being used to detect failure on future tax payment, is when is flagged by bank that a customer is missing payments regarding a loan or its ratting was put with a negative outlook. That event can be used by the government agency to start working with the individual or enterprise in order that will not exist missed tax revenue. The Know Your Tax Payer (KYTP), can be also used by all the entities that feed knowledge base, e.g. other banks that belong to the system (Telecom’s, Utilities, Insurance Companies) will also be alerted by a missed loan payment contributing to decrease their risk exposure, when debt carousel schemes are implemented. There are other scenarios that such an approach can also be beneficial, not only related with tax compliance. In terms of investment programs, related with shared responsibilities on entitlement of government funds, which access is granted by a government subsidy, as well as, by banks, even under a syndicated loan approach, using a KYTP will contribute access to grants or subsidies to entities that deserves such access and in better terms and change tax payer behavior.

The man and the machine manifesto

“Self-organised systems, lie all around us. There are quagmires, the fish in the sea, or intractable systems like clouds. Surely, we can make these work things out for us, act as our control mechanisms, perhaps most important of all, we can couple these seemingly uncontrollable entities together so that they can control each other. Why not, for example, couple the traffic chaos in Chicago to the traffic chaos in New York in order to obtain an acceptably self-organising whole? Why not associate brains to achieve a group intelligence?”

Gordon Pask, The natural history of networks

 

Today there is a shadow or sense of doubt if we as humans want machines to think or to do – some people already argue they do think, detached from the consciousness bond – and it would replace humanity soon, as many others advocate singularity is near with systems that can adapt themselves, command, control other systems, something as Douglas Hofstadter referred to “tangled recursion” as an example of machine intelligence.
I ultimately believe that humans and powered AI machines will work together, not compete against each other. Humans will be much more empowered by the symbiotic combination of machine work, meaning that we will need to continuous to adapt to rather than become indispensables, we increase our own capabilities.
However, there are many untamed challenges in terms of such man and machine symbiosis, as we the human species, have the responsibility to define by which rules we want to live, as machines progress in new areas, changing the foundations on our society is organized, as per bellow.

Governance
Much has been discussed how to define machine design rules and relevant regulation. Some experts believe that is the hands of humans to define such rules and while machines are tightly controlled by humans we can define how machines should be engineered. However, some examples related with war machines, demonstrate that Isaac Asimov’s laws do not apply anymore, once the potential for harm is increasing rapidly.
Hence the challenge stands. Consider the scenario of an intelligent medical system that provides counselling and advisory, induces a medical doctor with error. Who is responsible? The Doctor? The system? The entity that conceived the system? The trainer that trained the system to make decisions, based on a knowledge base? How do we deal with human life loss? Regulatory bodies for the engineering profession or other domain expertise, have clearly defined rules for design or professional act decisions, made by humans, however, in terms of machines endowed with any kind of intelligence, governance appears missing and there is no common broad agreement.

Societal impact, innovation and economy growth
There is no doubt that technology was always the common denominator that sparked economic growth, the press, steam engines and lastly the internet, created in three different moments in time tectonic shifts. However, prosperity also contributes to unemployment. Technology tends to automate at scale and replace repetitive tasks, but the last wave of technological developments is already targeting knowledge workers as well. Hence, the challenge is not related with low income workers only. The balancing act should be how the use of technology can contribute to higher living standards, diminish inequality and drive inclusion.

Human and Machine Interface
Mixed reality is becoming a popular interface in human-computer interaction for combining virtual and real-world environments, and it has recently been a common technique for human-robot interaction, it price is however a barrier for adoption and creates digital imparity. Natural language processing is becoming another de-facto interface, applied for example on business to consumer interactions, but some questions are still not addressed in terms of humans that speak a language blending influence of their own culture that a machine is not aware of. Despite the advance interacting with devices like smartphones, as technology progresses, it is relevant do involve interface designers to make a reflection how machines affect human to human interactions and human to machine interactions.

Ethics
Tackling the trade-off between privacy and security is today already a challenge, related to the fundamental but complex separation between what constitutes the private and the public space of an individual. The definition of a concept, a domain, is a consequence of the surroundings, of the environment we live and the multitude of human principles and beliefs. What in a society can be accepted as a practice, in another can be condemned. The concept of privacy is constantly being redefined to a point that can be transform into a matter of transparency, for example, sharing publicly your taxes declarations if you are a politician. How we deal with ethics in terms of a machine that have access and share our medical records that will make decisions in terms of triage or sense of urgency related with medical treatment? It’s in ethical that a machine can make judgment about predicting future crimes or provide a credit risk score based on data that is related with our profiles?